RESUMO
BACKGROUND: The gold standard method for assessing the QTcF (QT corrected for heart rate by Fridericia's cube root formula) interval is the "QTcF semiautomated triplicate averaging method" (TAM), which consists of measuring three QTcF values semiautomatically, for each 10-second sequence of a triplicate electrocardiogram set, and averaging them to get a global and unique QTcF value. Thus, TAM is time consuming. We have developed a new method, namely the "QTcF semiautomated triplicate concatenation method" (TCM), which consists of concatenating the three 10-second sequences of the triplicate electrocardiogram set as if they were a single 30-second electrocardiogram, and measuring QTcF only once for the triplicate electrocardiogram set. AIM: To compare the TCM method with the TAM method. METHODS: Fifty triplicate electrocardiograms were read twice by an expert and a student using both methods (TAM and TCM). We plotted Bland-Altman plots to assess agreement between the two methods, and to compare the student and expert results. The time needed to read a set of 20 consecutive triplicate electrocardiograms was measured. RESULTS: Limits of agreement between TAM and TCM ranged from -8.25 to 6.75ms with the expert reader. TCM was twice as fast as TAM (17.38 versus 34.28min for 20 consecutive triplicate electrocardiograms). Bland-Altman plots comparing student and expert results showed limits of agreement ranging from -4.34 to 11.75ms for TAM, and -1.2 to 8.0ms for TCM. CONCLUSIONS: TAM and TCM show good agreement for QT measurement. TCM is less time consuming than TAM. After a learning session, an inexperienced reader can measure the QT interval accurately with both methods.
RESUMO
BACKGROUND: The clinical course and the precipitating risk factors in the congenital long QT syndrome (LQTS) are genotype specific. OBJECTIVES: The goal of this study was to develop a computer algorithm allowing for electrocardiogram (ECG)-based identification and differentiation of LQT1 and LQT2 carriers. METHODS: Twelve-lead ECG Holter monitor recordings were acquired in 49 LQT1 carriers, 25 LQT2 carriers, and 38 healthy subjects as controls. The cardiac beats were clustered based on heart-rate bin method. Scalar and vectorial repolarization parameters were compared for similar heart rates among study groups. The Q to Tpeak (QTpeak), the Tpeak to Tend interval, T-wave magnitude and T-loop morphology were automatically quantified using custom-made algorithms. RESULTS: QTpeak from lead II and the right slope of the T-wave were the most discriminant parameters for differentiating the 3 groups using prespecified heart rate bin (75.0 to 77.5 beats/min). The predictive model utilizing these scalar parameters was validated using the entire spectrum of heart rates. Both scalar and vectorcardiographic models provided very effective identification of tested subjects in heart rates between 60 and 100 beats/min, whereas they had limited performance during tachycardia and slightly better discrimination in bradycardia. In the 60 to 100 beats/min heart rate range, the best 2-variable model identified correctly 89% of healthy subjects, 84% of LQT1 carriers, and 92% of LQT2 carriers. A model including 3 parameters based purely on scalar ECG parameters could correctly identify 90% of the population (89% of healthy subjects, 90% of LQT1 carriers, and 92% of LQT2 carriers). CONCLUSION: Automatic algorithm quantifying T-wave morphology discriminates LQT1 and LQT2 carriers and healthy subjects with high accuracy. Such computerized ECG methodology could assist physicians evaluating subjects suspected for LQTS.